A major objective of the present invention is to enhance the ability of an automated workload manager to dynamically allocate resources to workloads to maximize a selected measure of desirability. The computing resources required by software workloads can vary considerably over time. Providing sufficient resources to each workload full time to handle occasional peaks can be wasteful. Dynamic workload management shifts computing resources among workloads on an as-needed basis; as a result, workload requirements can be met with fewer total resources and, thus, at lower cost.
Workload management software automatically allocates resources in accordance with “management policies” designed to optimize the value of the computer system to the user. The policies can provide a great deal of flexibility for a user to determine how resources are to be allocated and what factors should be taken into account. For example, a simple utilization-based policy calls for shifting resources from workloads with lower percentage utilizations of a resource to workloads with higher percentage utilizations.
Workload management requires predicting for an upcoming allocation period an amount of a computing resource required to meet some target performance level for a workload. Resources are then allocated at least in part as a function of demand levels for the workloads predicted at least in part based on resource consumption histories. However, as the future is uncertain, future demand can be better represented by a range of demand levels with different probabilities of occurring, in other words, by a probability distribution of demand levels. Workload management schemes that use only a single predicted demand level effectively discard distribution data that might otherwise be useful in determining an optimal allocation of resources to workloads.
Currently pending U.S. patent application Ser. No. 11/752,231 to Blanding, discloses a “multi-prediction” workload management method that takes advantage of some of the additional information available in a probability distribution. This method provides for the generation of multiple resource requests with reduced priorities as a means of incorporating uncertainty concerning workload resource demands into the allocation process. However, the present invention offers, at least in some cases, a more effective approach to incorporating demand-level probability distribution information in resource allocation.
Herein, related art is described to facilitate understanding of the invention. Related art labeled “prior art” is admitted prior art; related art not labeled “prior art” is not admitted prior art.